Social vulnerability assessment using artificial neural network (ANN) model for earthquake hazard in Tabriz city, Iran

• Main Authors: Alizadeh, Mohsen, Alizadeh, Esmaeil, Kotenaee, Sara Asadollahpour, Shahabi, Himan, Pour, Amin Beiranvand, Panahi, Mahdi, Ahmad, Baharin, Saro, Lee
• Format: Article
• Language: English
• Published: MDPI AG 2018
• Subjects: HT101-395 Sociology, Urban
• Online Access: http://eprints.utm.my/id/eprint/86196/1/MohsenAlizadeh2018_SocialVulnerabilityAssessmentUsingArtificialNeural.pdf; http://eprints.utm.my/id/eprint/86196/; http://dx.doi.org/10.3390/su10103376
• Institution: Universiti Teknologi Malaysia

This study presents the application of an artificial neural network (ANN) and geographic information system (GIS) for estimating the social vulnerability to earthquakes in the Tabriz city, Iran. Thereby, seven indicators were identified and used for earthquake vulnerability mapping, including population density, household density, employed density, unemployed density, and literate people. To obtain more accuracy in our analysis, all of the indicators were entered into a geographic information system (GIS). After the standardization of the data, an artificial neural network (ANN) model was applied for deriving a social vulnerability map (SVM) of different hazard classes for Tabriz city. The results showed that 0.77% of the total area was found to be very highly vulnerable. Very low vulnerability was recorded for 76.31% of the total study area. The comparison of data provided by (SVM) and the residential building vulnerability (RBV) of Tabriz city indicated the validity of the results obtained by ANN processes. Scatter plots are used to plot the data. These scatter plots indicate the existence of a strong positive relationship between the most vulnerable zones (1, 4, and 5) and the least (3, 7, and 9) of the SVM and RBV. The results highlight the importance of using social vulnerability study for defining seismic-risk mitigation policies, emergency management, and territorial planning in order to reduce the impacts of disasters.